Development of Next-Generation Semi-structured Data Mining for Large-Scale Knowledge Base Formation

2015 Fiscal Year Final Research Report

• Project/Area Number: 24240021
• Research Category: Grant-in-Aid for Scientific Research (A)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Intelligent informatics
• Research Institution: Hokkaido University
• Principal Investigator: Arimura Hiroki 北海道大学, 情報科学研究科, 教授 (20222763)
• Co-Investigator(Kenkyū-buntansha): UNO TAKEAKI 国立情報学研究所, 情報学プリンシプル研究系, 教授 (00302977) ; MINATO S. 北海道大学, 大学院情報科学研究科, 教授 (10374612) ; HIRATA KOUICHI 九州工業大学, 情報工学研究院, 教授 (20274558) ; ITO K. 北海道大学, 人獣共通感染症リサーチセンター, 教授 (60396314) ; SHIMOZONO S. 九州工業大学, 情報工学研究院, 准教授 (70243988) ; KIDA T. 北海道大学, 大学院情報科学研究科, 准教授 (70343316)
• Project Period (FY): 2012-04-01 – 2016-03-31
• Keywords: 大規模半構造データ / データマイニング / 高次元データ検索 / イベントストリーム処理 / 知識索引 / 知識発見 / ビッグデータ

Outline of Final Research Achievements

The final goal of this research is to establish a strategy for forming large-scale knowledge bases from massive data and information on a wide range of human activities on social, scientific, and industrial aspects in the cyber space. For this purpose, we study next-generation data mining technologies for efficiently extracting useful knowledge as patterns and rules from semi-structured data, that is, huge and heterogeneous collections of weakly structured data in the cyber space. (1) Efficient semi-structured data mining engines based on optimal pattern discovery framework. (2) Semi-strcuture mining based on spatio-temporal information. (3) Combining semi-structured data mining with stochastic information processing schema. (4) Knowledge federation technologies for large-scale knowledge bases creation. (5) Knowledge indexing technologies for large-scale knowledge bases creation. (6) Development of knowledge base creation systems based on semi-structured data mining.

Free Research Field

知能情報学